A change-point problem and inference for segment signals
ESAIM: Probability and Statistics, Tome 22 (2018), pp. 210-235.

We address the problem of detection and estimation of one or two change-points in the mean of a series of random variables. We use the formalism of set estimation in regression: to each point of a design is attached a binary label that indicates whether that point belongs to an unknown segment and this label is contaminated with noise. The endpoints of the unknown segment are the change-points. We study the minimal size of the segment which allows statistical detection in different scenarios, including when the endpoints are separated from the boundary of the domain of the design, or when they are separated from one another. We compare this minimal size with the minimax rates of convergence for estimation of the segment under the same scenarios. The aim of this extensive study of a simple yet fundamental version of the change-point problem is two-fold: understanding the impact of the location and the separation of the change points on detection and estimation and bringing insights about the estimation and detection of convex bodies in higher dimensions.

Accepté le :
DOI : 10.1051/ps/2018014
Classification : 62F10, 60G55
Mots-clés : Change-point, detection, hypothesis testing, minimax, separation rate, set estimation
Brunel, Victor-Emmanuel 1

1
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Brunel, Victor-Emmanuel. A change-point problem and inference for segment signals. ESAIM: Probability and Statistics, Tome 22 (2018), pp. 210-235. doi : 10.1051/ps/2018014. http://www.numdam.org/articles/10.1051/ps/2018014/

[1] J. Antoch and M. Hušková, Bayesian-type estimators of change points. Prague Workshop on Perspectives in Modern Statistical Inference: Parametrics, Semi-parametrics, Non-parametrics 1998. J. Stat. Plan. Inference 91 (2000) 195–208. | DOI | MR | Zbl

[2] A. Betken, Testing for change-points in long-range dependent time series by means of a self-normalized Wilcoxon test. J. Time Ser. Anal. 37 (2016) 785–809. | DOI | MR | Zbl

[3] A.A. Borovkov and Yu.Yu. Linke, Asymptotically optimal estimates in the smooth change-point problem. Math. Method. Stat. 13 (2004) 1–24. | MR | Zbl

[4] V.-E. Brunel, Adaptive estimation of convex polytopes and convex sets from noisy data. Electron. J. Stat. 7 (2013) 1301–1327. | MR | Zbl

[5] J. Chen and A.K. Gupta, Statistical inference of covariance change points in gaussian model. Statistics 38 (2004) 17–28. | DOI | MR | Zbl

[6] H.P. Chan and G. Walther, Detection with the scan and the average likelihood ratio. Stat. Sin. 23 (2013) 409–428. | MR | Zbl

[7] T.T. Cai and M. Yuan, Rate-optimal detection of very short signal segments. Technical report (2014).

[8] K. Frick, A. Munk and H. Sieling, Multiscale change point inference. J. R. Stat. Soc. Ser. B. Stat. Methodol. 76 (2014) 495–580. With 32 discussions by 47 authors and a rejoinder by the authors. | DOI | MR | Zbl

[9] S. Gaïffas, Convergence rates for pointwise curve estimation with a degenerate design. Math. Method. Stat. 14 (2005) 1–27. | MR

[10] G. Gayraud, Minimax hypothesis testing about the density support. Bernoulli 7 (2001) 507–525. | DOI | MR | Zbl

[11] K. Gadeikis and V. Paulauskas, On the estimation of a changepoint in a tail index. Liet. Mat. Rink. 45 (2005) 333–348. | MR | Zbl

[12] I.A. Ibragimov and R.Z. Khasminskii, Estimation of the parameter of a discontinuous signal in Gaussian white noise. Prob. Peredači Inform. 11 (1975) 31–43. | MR | Zbl

[13] I.A. Ibragimov and R.Z. Khasminskii, Statistical Estimation: Asymptotic Theory. Springer Science & Business, New York, (2013).

[14] A.P. Korostelev, On minimax estimation of a discontinuous signal (translation of a paper in russian, published in 1986). Theory Probab. Appl. 32 (2006) 727–730. | DOI | Zbl

[15] A.P. Korostelëv and A.B. Tsybakov, Asymptotically minimax image reconstruction problems, in Topics in Nonparametric Estimation. Vol. 12 of Advances in Soviet Mathematics. American Mathematical Society, Providence, RI (1992) 45–86. | DOI | MR | Zbl

[16] A.P. Korostelëv and A.B. Tsybakov, Minimax theory of image reconstruction. Vol. 82 of Lecture Notes in Statistics. Springer-Verlag, New York (1993). | DOI | MR | Zbl

[17] E. Lebarbier, Detecting multiple change-points in the mean of gaussian process by model selection. Signal Process. 85 (2005) 717–736. | DOI | Zbl

[18] S. Lee, J. Ha, O. Na and S. Na, The cusum test for parameter change in time series models. Scand. J. Stat. 30 (2003) 781–796. | DOI | MR | Zbl

[19] O.V. Lepski and A.B. Tsybakov, Asymptotically exact nonparametric hypothesis testing in sup-norm and at a fixed point. Probab. Theory Rel. Fields 117 (2000) 17–48. | DOI | MR | Zbl

[20] Y. Ninomiya, Information criterion for Gaussian change-point model. Stat. Probab. Lett. 72 (2005) 237–247. | DOI | MR | Zbl

[21] C.-W. Park and W.-C. Kim, Estimation of a regression function with a sharp change point using boundary wavelets. Stat. Probab. Lett. 66 (2004) 435–448. | DOI | MR | Zbl

[22] M. Raimondo, Minimax estimation of sharp change points. Ann. Stat. 26 (1998) 1379–1397. | DOI | MR | Zbl

[23] Y.S. Son and S.W. Kim, Bayesian single change point detection in a sequence of multivariate normal observations. Statistics 39 (2005) 373–387. | DOI | MR | Zbl

[24] X. Shao and X. Zhang, Testing for change points in time series. J. Am. Stat. Assoc. 105 (2010) 1228–1240. | DOI | MR | Zbl

[25] A.B. Tsybakov, Introduction to nonparametric estimation. Revised and extended from the 2004 French original, Translated by Vladimir Zaiats. Springer Series in Statistics. Springer, New York (2009). | DOI | MR | Zbl

[26] Y. Wu, Inference for change-point and post-change mean with possible change in variance. Seq. Anal. 24 (2005) 279–302. | DOI | MR | Zbl

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